code: def cumulative_state_net(
      X,
      name,
      use_fp16,
      pdrop,
      train,
      pool_kernel_size=2,
      nominal_pool_length=512,
      use_fused_kernel=True,
  ):
      conv_kernel = 4
      pool_kernel_size = pool_kernel_size or conv_kernel
  
      nx = shape_list(X)[-1]
      with tf.compat.v1.variable_scope(name):
          output = tf.nn.relu(
              normal_1d_conv_block(
                  X,
                  conv_kernel,
                  "1-" + str(conv_kernel),
                  use_fp16,
                  output_dim=nx,
              )
          )
          output = tf.nn.relu(
              normal_1d_conv_block(
                  output,
                  conv_kernel,
                  "2-" + str(conv_kernel),
                  use_fp16,
                  output_dim=nx,
              )
          )
          output = normal_1d_conv_block(
              output,
              conv_kernel,
              "3-" + str(conv_kernel),
              use_fp16,
              output_dim=nx,
          )
  
      output = dropout(output, pdrop, train)
      aggregated = cascaded_pool(
          output,
          kernel_size=pool_kernel_size,
          pool_len=nominal_pool_length,
          use_fused_kernel=use_fused_kernel,
      )
  
      return normal_1d_conv_block(
          aggregated, 1, "output_reproject", use_fp16, output_dim=nx
      )
  
file_path: finetune/finetune/base_models/oscar/featurizer.py
imports: import tensorflow
  from finetune.base_models.gpt.featurizer import dropout, embed, split_heads, merge_heads
  from finetune.util.shapes import shape_list, lengths_from_eos_idx
  from finetune.custom_ops import recursive_agg
  from finetune.base_models.gpt.featurizer import get_pos_values
  from finetune.optimizers.recompute_grads import recompute_grad
  import functools
label_desc: Use image node_id 7 for calling a global function with example usage: cumulative_state_net(X, name, use_fp16, pdrop, train, pool_kernel_size, nominal_pool_length, use_fused_kernel) and returns: normal_1d_conv_block